Summary
Scale prevention is important to ensure continuous production from existing reserves that produce brine. Wells could be abandoned prematurely because of poor management of scale and corrosion. The objective of this paper is to present an overview of scale prediction and control and the current research at Rice University to solve these problems. In this paper, the challenges of scale prediction at high temperature, high pressure, and high total dissolved solids (TDS) and an accurate model to predict pH, scale indices, density, and inhibitor needs at these conditions are discussed and reviewed: specifically discussed are (1) the various scale types found in oil and gas production and the condition under which they form; (2) the relationship of pH, alkalinity, organic acids, carbonates, and CO2 distribution; (3) the temperature (T), pressure (P), TDS dependence of the thermodynamic equilibrium constants and activity coefficients; and (4) the accuracy of the Pitzer ion-interaction model-based scale-prediction algorithms and their application. On the basis of a simple propagation of error estimation, the overall estimated error for calcite saturation index (SI) is ± 0.1. This algorithm has been validated with literature solubility data for six minerals in the T, P, and TDS range of 0 to 200°C, 0 to 15,000 psia, and 0 to 350 000 mg/L TDS; for pH data at 25 and 60°C; and density of weighting fluids with density between 8 and 12.7 lbm/gal.
Improving the energy efficiency of oil production using identification and prediction of operating modes of production wells based on data analysis methods, machine learning and neural networks
